ABSTRACT An algorithm that predicts lung function decline in patients with chronic lung disease is highly desirable. We have developed a blood biomarker-based platform that predicts lung function decline in CF patients with the potential to inform therapeutic interventions to prevent/slow decline, shorten the length (reducing cost) of clinical trials, and accelerate the examination of potential therapies allowing faster delivery to the patients. Furthermore, the behavior of molecular biomarkers can inform therapy and increase success of alleviating disease progression. We have developed a blood biomarker-informed algorithm that predicts functions decline and propose here to deliver it to the CF care community by achieving the 3 milestone; 1) (R61 Phase, Year 1): Complete the design and testing of proteomics-informed GUI that integrates routinely collected clinical/demographic data with marker data to generate real-time risk prediction of lung function decline and field test GUI utility using the CF learning network, 2) (R33 Phase, Year 2): Complete a validation of a clinical test of our markers in a large cohort of banked longitudinal samples at CCHMC and externally at Battelle Memorial Institute, and 3) (R33 Phase, Year 3): Complete the selection of the optimal algorithm parameters that provide the best specificity and sensitivity for our markers individually and/or in combination. Development, validation, and dissemination of our proteomics-informed risk prediction platform will set the stage for a pre-submission meeting with the FDA for commercial translation of the technology and its delivery to the CF care and patient community.